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# MobileNets
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNetV2: Inverted Residuals and Linear Bottlenecks
**Note**:
- `image_classification_mobilenetvX_2022apr_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
Results of accuracy evaluation with [tools/eval](../../tools/eval).
| Models | Top-1 Accuracy | Top-5 Accuracy |
| ------------------ | -------------- | -------------- |
| MobileNet V1 | 67.64 | 87.97 |
| MobileNet V1 block | 67.21 | 87.62 |
| MobileNet V1 quant | 55.53 | 78.74 |
| MobileNet V2 | 69.44 | 89.23 |
| MobileNet V2 block | 68.66 | 88.90 |
| MobileNet V2 quant | 68.37 | 88.56 |
\*: 'quant' stands for 'quantized'.
\*\*: 'block' stands for 'blockwise quantized'.
## Demo
### Python
Run the following command to try the demo:
```shell
# MobileNet V1
python demo.py --input /path/to/image
# MobileNet V2
python demo.py --input /path/to/image --model v2
# get help regarding various parameters
python demo.py --help
```
### C++
Install latest OpenCV and CMake >= 3.24.0 to get started with:
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# detect on camera input
./build/opencv_zoo_image_classification_mobilenet
# detect on an image
./build/opencv_zoo_image_classification_mobilenet -m=/path/to/model -i=/path/to/image -v
# get help messages
./build/opencv_zoo_image_classification_mobilenet -h
```
## License
All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
## Reference
- MobileNet V1: https://arxiv.org/abs/1704.04861
- MobileNet V2: https://arxiv.org/abs/1801.04381
- MobileNet V1 weight and scripts for training: https://github.com/wjc852456/pytorch-mobilenet-v1
- MobileNet V2 weight: https://github.com/onnx/models/tree/main/vision/classification/mobilenet
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